Abstract
In their most aggressive form, the mortality rate of gliomas is high. Accurate segmentation is important for surgery and treatment planning, as well as for follow-up evaluation. In this paper, we propose to segment brain tumors using a Deep Convolutional Neural Network. Neural Networks are known to suffer from overfitting. To address it, we use Dropout, Leaky Rectifier Linear Units and small convolutional kernels. To segment the High Grade Gliomas and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.
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References
Agn, M., Puonti, O., Law, I., af Rosenschöld, P.M., van Leemput, K.: Brain tumor segmentation by a generative model with a prior on tumor shape. In: Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 1–4 (2015)
Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., Davazikos, C.: Segmentation of gliomas in multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework. In: Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 5–12 (2015)
Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)
Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of mri-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Dieleman, S., Schlter, J., Raffel, C., Olson, E., Snderby, S.K., Nouri, D., Maturana, D., Thoma, M., Battenberg, E., Kelly, J., Fauw, J.D., Heilman, M., diogo149, McFee, B., Weideman, H., takacsg84, peterderivaz, Jon, instagibbs, Rasul, D.K., CongLiu, Britefury, Degrave, J.: Lasagne: First release, August 2015. http://dx.doi.org/10.5281/zenodo.27878
Dvorák, P., Menze, B.: Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. In: Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 13–24 (2015)
Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.: Glistr: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31(10), 1941–1954 (2012)
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. arXiv preprint (2015). arXiv:1505.03540
Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013). http://www.jmir.org/2013/11/e245/
Kwon, D., Shinohara, R.T., Akbari, H., Davatzikos, C.: Combining generative models for multifocal glioma segmentation and registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 763–770. Springer, Heidelberg (2014)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lee, C.-H., Wang, S., Murtha, A., Brown, M.R.G., Greiner, R.: Segmenting brain tumors using pseudo–conditional random fields. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 359–366. Springer, Heidelberg (2008)
Lyksborg, M., Puonti, O., Agn, M., Larsen, R.: An ensemble of 2D convolutional neural networks for tumor segmentation. In: Paulsen, R.R., Pedersen, K.S. (eds.) SCIA 2015. LNCS, vol. 9127, pp. 201–211. Springer, Heidelberg (2015)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the ICML, vol. 30 (2013)
Meier, R., Bauer, S., Slotboom, J., Wiest, R., Reyes, M.: Appearance-and context-sensitive features for brain tumor segmentation. In: BraTS Challenge Manuscripts, pp. 20–26 (2014)
Meier, R., Karamitsou, V., Habegger, S., Wiest, R., Reyes, M.: Parameter learning for crf-based tissue segmentation of brain tumors. In: Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 48–51 (2015)
Menze, B., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B., Ayache, N., Buendia, P., Collins, D., Cordier, N., Corso, J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Durst, C., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K., Jena, R., John, N., Konukoglu, E., Lashkari, D., Mariz, J., Meier, R., Pereira, S., Precup, D., Price, S., Riklin Raviv, T., Reza, S., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C., Shotton, J., Silva, C., Sousa, N., Subbanna, N., Szekely, G., Taylor, T., Thomas, O., Tustison, N., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Menze, B.H., van Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 151–159. Springer, Heidelberg (2010)
Nyúl, L.G., Udupa, J.K., Zhang, X.: New variants of a method of mri scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)
Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D.M., Silva, C.A.: Brain tumour segmentation based on extremely randomized forest with high-level features. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3037–3040. IEEE (2015)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint. (2014). arXiv:1409.1556
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Tustison, N.J., Shrinidhi, K., Wintermark, M., Durst, C.R., Kandel, B.M., Gee, J.C., Grossman, M.C., Avants, B.B.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with antsr. Neuroinformatics pp. 1–17 (2014)
Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J.: Multi-modal brain tumor segmentation using deep convolutional neural networks. In: MICCAI Brain Tumor Segmentation Challenge (BraTS), pp. 1–5 (2014)
Van Meir, E.G., Hadjipanayis, C.G., Norden, A.D., Shu, H.K., Wen, P.Y., Olson, J.J.: Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J. Clin. 60(3), 166–193 (2010)
Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012)
Acknowledgments
This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. Sérgio Pereira was supported by a scholarship from Fundação para a Ciência e Tecnologia (FCT), Portugal (scholarship number PD/BD/105803/2014). Brain tumor image data used in this article were obtained from the MICCAI 2013 Challenge on Multimodal Brain Tumor Segmentation. The challenge database contain fully anonymized images from the Cancer Imaging Archive.
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Pereira, S., Pinto, A., Alves, V., Silva, C.A. (2016). Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_12
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DOI: https://doi.org/10.1007/978-3-319-30858-6_12
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